Over the past decades, skin cancer has emerged as a pressing challenge in public health, accountable for 5.4 million new cases in the United States [1]}, [2]}.
Melanoma, the most serious type of skin cancer, holds a mortality rate of 75% [3]}.
Due to the dauntingly high incidence and mortality rates, early detection and prevention of skin cancer are critical.
In response, recent studies have developed automated skin cancer diagnosis approaches, which achieved performance on par with board-certified dermatologists [4]}, [5]}.
However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation.
In essence, the category of a skin lesion is determined by its asymmetry, border, intensity, and physical size [6]}, which rely heavily on accurate lesion segmentation results.
A faithful classification, on the other hand, can also serve as crucial guidance of lesion segmentation by extracting discriminant lesion features from the dermoscopic image.
Naturally, in this paper, we seek to answer this critical question:
How to beneficially integrate the task of segmentation with classification for skin cancer diagnosis?
